def setUp(self): # box peak info = {} trace = Trace(np.ones(10), np.arange(10), name=1) baseline = Trace([0, 9], [0, 0], name=1) c = PeakComponent(info, trace, baseline) self.peak = Peak('box', components=c)
def read_amdis_list(db, filename): def get_val(line, cols, key): return line.split('\t')[cols.index(key)] cmp_lvl = 2 # number of directory levels to compare # TODO: does this work for agilent files only? mapping = defaultdict(list) with open(filename, 'r') as f: cols = f.readline().split('\t') for line in f: filename = get_val(line, cols, 'FileName') fn = op.splitext('/'.join(filename.split('\\')[-cmp_lvl:]))[0] # find if filtered filename overlaps with anything in the db for dt in db.children_of_type('file'): if fn in '/'.join(dt.rawdata.split(op.sep)): break else: continue info = {} info['name'] = get_val(line, cols, 'Name') info['p-s-time'] = get_val(line, cols, 'RT') info['p-s-area'] = get_val(line, cols, 'Area') ts = Chromatogram(np.array([np.nan]), np.array([np.nan]), ['']) mapping[dt] += [Peak(info, ts)] with db: for dt in mapping: dt.children += mapping[dt]
def setUp(self): # box peak info = {} v = [ 0.043, 0.067, 0.094, 0.117, 0.131, 0.131, 0.117, 0.094, 0.067, 0.043 ] trace = Trace(v, np.arange(10), name=1) baseline = Trace([0, 9], [0, 0], name=1) c = PeakComponent(info, trace, baseline) self.peak = Peak('gaussian', components=c)
def read_peaks(db, filename, ftype='isodat'): if ftype is None: with open(filename, 'r') as f: header = f.readline() if 'd 13C/12C[per mil]vs. VPDB' in header: ftype = 'isodat' else: ftype = 'amdis' if ftype == 'amdis': delim = '\t' cvtr = {'name': 'name', 'p-s-time': 'rt', 'p-s-area': 'area'} elif ftype == 'isodat': delim = ',' cvtr = {'name': 'peak nr.', 'p-s-time': 'rt[s]', 'p-s-area': 'area all[vs]', 'p-s-width': 'width[s]', 'p-s-d13c': 'd 13c/12c[per mil]vs. vpdb', 'p-s-d18o': 'd 18o/16o[per mil]vs. vsmow'} headers = None mapping = defaultdict(list) ref_pk_info = {} def get_val(line, cols, key): return line.split(delim)[cols.index(key)] with open(filename, 'r') as f: for line in f: if bool(re.match('filename' + delim, line, re.I)) or headers is None: headers = line.lower().split(',') continue fn = get_val(line, headers, 'filename') if ftype == 'amdis': # AMDIS has '.FIN' sufffixes and other stuff, so # munge Filename to get it into right format cmp_lvl = 2 fn = op.splitext('/'.join(fn.split('\\')[-cmp_lvl:]))[0] # find if filtered filename overlaps with anything in the db for dt in db.children_of_type('file'): if fn in '/'.join(dt.rawdata.split(op.sep)): break else: continue info = {} # load all the predefined fields for k in cvtr: info[k] = get_val(line, headers, cvtr[k]) # create peak shapes for plotting if ftype == 'isodat': rt = float(info['p-s-time']) / 60. width = float(info['p-s-width']) / 60. t = np.linspace(rt - width, rt + width) data = [] for ion in ['44', '45', '46']: area = float(get_val(line, headers, 'rarea ' + ion + '[mvs]')) / 60. # bgd = float(get_val(line, headers, \ # 'bgd ' + ion + '[mv]')) height = float(get_val(line, headers, 'ampl. ' + ion + '[mv]')) # save the height at 44 into the info for linearity if ion == '44': info['p-s-ampl44'] = height # 0.8 is a empirical number to make things look better data.append(gaussian(t, x=rt, w=0.5 * area / height, h=height)) # save info if this is the main ref gas peak if info['name'].endswith('*'): ref_pk_info[dt] = info ts = Chromatogram(np.array(data).T, t, [44, 45, 46]) else: ts = Chromatogram(np.array([np.nan]), np.array([np.nan]), ['']) mapping[dt] += [Peak(info, ts)] # do drift correction if ftype == 'isodat': for dt in mapping: ref_pks = [] hgt44 = ref_pk_info[dt]['p-s-ampl44'] d18o = float(ref_pk_info[dt]['p-s-d18o']) d13c = float(ref_pk_info[dt]['p-s-d13c']) for pk in mapping[dt]: # if the d18o and height are similar, it's a ref peak if abs(pk.info['p-s-ampl44'] - hgt44) < 10. and \ abs(float(pk.info['p-s-d18o']) - d18o) < 2.: ref_pks.append(pk) # get out the dd13C values and times for the ref gas peaks d13cs = [float(pk.info['p-s-d13c']) for pk in ref_pks] dd13cs = np.array(d13cs) - d13c rts = [float(pk.info['p-s-time']) for pk in ref_pks] # try to fit a linear model through all of them p0 = [d13cs[0], 0] def errfunc(p, x, y): return p[0] + p[1] * x - y try: p, succ = leastsq(errfunc, p0, args=(np.array(rts), dd13cs)) except Exception: p = p0 # apply the linear model to get the dd13C linearity correction # for a given time and add it to the value of this peak for pk in mapping[dt]: pk.info['p-s-d13c'] = str(-errfunc(p, float(pk.info['p-s-time']), float(pk.info['p-s-d13c']))) # save everything with db: for dt in mapping: dt.children += mapping[dt]